Professional Practice in Sport Performance Analysis

1. 1Name the construct to yourself in working terms: micropolitics is the day-to-day social reality of staff (people, opinions, attitudes, personalities), and micropolitical literacy is your ability to navigate it. Treat the coaching process as 'inherently messy and disordered at times', not as a linear model you can run unchanged.
2. 2Map the people you depend on. List the stakeholders whose agendas touch your output: coach, players, other coaches, managers, owners, sport science staff. Note for each what they need from analysis and what they can do to your standing, since coaching is described as power-ridden and managing these micro-relations is a core part of the job.
3. 3Read your own interpretive lens first. Write down your subjective theory (your 'know how' about the job) and your personal interpretive framework (the career-built beliefs through which you read situations). Check whether your reading of a conflict comes from the facts or from that lens.
4. 4Sort each live issue into one of the interest types: organisational (job retention, structures, procedures, job offers), cultural-ideological (norms, values, culture) or social-professional (trust, conflict, suspicion, gossip). Job uncertainty threatens self-esteem and competence, so flag any issue that touches your own security.
5. 5Anchor your work in specific, measurable performance questions agreed with the coach. A general analysis is too vague and invites competing readings. A short list of agreed core questions gives you a defensible mandate.
6. 6Invest time in the coach and their coaching philosophy (not the playing philosophy), the game plan and tactical preferences. Spend quality time with coaches and players. This builds the relationships and credibility that protect you when agendas collide.
7. 7Choose a strategy deliberately, as reactive (maintain, negotiate, protect your interests) or proactive (improve conditions). Behaviours such as talking, pleading, flattering, staying silent, avoiding sides, accepting extra duties and using humour can all be political. Because almost any action can become micropolitically meaningful, judge the effect in context rather than from a list.
8. 8Manage overlapping cycles with explicit expectations. You may be finishing post-match for one game, building pre-match weeks ahead and doing long-term profiling at once. Say what is due, when, and what is not being done, so time and output expectations are set by you rather than assumed.

The passages describe a change in the environment that tests micropolitical literacy. Technology is changing the analyst's role, with automation freeing time for analysis instead of laborious data collection. Adopting it means admitting that some coding and data-extraction habits may be outdated, which puts you in a vulnerable position and requires humility. Take an analyst in this position. They first reflect on whether their practice suits the context and is open enough for collaboration. They then agree a small set of measurable performance questions with the coach, so the new capacity serves defined questions rather than a general analysis. They invest time in understanding the coach's philosophy, so the new outputs fit how the coach teaches. They also keep an expectation conversation going about overlapping cycles, namely post-match, pre-match and long-term profiling. The reflection, the question-setting and the relationship time together protect the analyst's position while the role shifts.

• If a request is a general or vague analysis, then work with the coach to turn it into one or more specific, measurable performance questions before starting.
• If you are running post-match, pre-match and long-term profiling at once, then state your capacity and agree expectations on time and output up front.
• If an issue threatens your job security or standing (organisational interest), then recognise it may be distorting your judgement, and pick a reactive or proactive strategy on purpose instead of defaulting.
• If a coach disputes your output or seems unreceptive, then check your understanding of their coaching philosophy and game plan before defending the data.
• If your methods are outdated or limit collaboration with other disciplines, then accept the vulnerable position and change practice rather than defend a single 'truth'.

• Treating the coaching process as linear and context-free, so you are blindsided by power dynamics. The tell: you are repeatedly surprised by decisions that ignore your work.
• Delivering broad analysis with no agreed question. The tell: coaches say it is interesting but nobody acts on it.
• Relying on a fixed list of tactics, such as flattery or silence, as if it covered every situation. The tell: the same move is applied everywhere and starts to backfire in a new context.
• Knowing the data but not the coach's philosophy, the game plan or the people. The tell: you could not deliver the insight if your presentation were deleted.
• Letting job uncertainty or mistrust drive reactive, self-protective behaviour. The tell: you avoid raising issues, join gossip or take every extra duty to stay safe, and your self-esteem and professional confidence erode.

• ▸Stakeholder and agenda: who is involved, and what do they want from analysis?
• ▸Power and resources: what can they grant or withdraw (access, time, budget, staff, standing)?
• ▸Interest type: organisational, cultural-ideological or social-professional?
• ▸Vulnerability check: does this touch my job security or confidence, and how might that colour my read?
• ▸Coach philosophy and game plan notes: what do I know and what must I learn?
• ▸Agreed performance question(s): specific and measurable, agreed with whom?
• ▸Cycle load: what is due now across post-match, pre-match and long-term profiling?
• ▸Chosen strategy: reactive or proactive, and what action specifically?
• ▸Review: what happened, and what would I adjust next time?

1. 1Separate the three cost lines before you plan anything: hardware (capture, cameras, devices), software subscriptions (the platforms you analyse and replay in), and human resource (analyst time and staffing). The source treats analysis as an expensive discipline, and a budget that names only one line hides the others.
2. 2Treat money as one resource among several. Leftwich's framing, used in the micropolitics chapter, counts material resources (equipment, money) and non-material ones (time, status, support, opportunity). Write down which of these you have and which you lack, because a shortage of time or support limits provision as much as a shortage of cash.
3. 3Define the depth and speed of provision the coaching process needs: what has to be delivered live, what can wait for post-session review, and what is a want rather than a need. Spend goes on the first group only. The source stresses that analysts must know where they sit in the coaching process cycle and whether their message fits it.
4. 4Match tools to those requirements. The text catalogues live replay in training, live video and data in matches, multi-angle capture, timeline coding and telestrated video. Pick the narrowest capability that serves the need, since the source calls some of the latest products 'astronomical' in price.
5. 5Ask for evidence before you buy. The source reports no clear consensus that technology has a positive linear effect on the coaching process, and research grounded in pedagogical principles is lacking. Write down what you expect the purchase to change in coaching behaviour, and how you will judge it afterwards.
6. 6Cost the people, not just the kit. Compare a salaried analyst, a paid placement year or a postgraduate placement embedded in a taught course. The source notes that unpaid internships are now widely questioned on ethical grounds, and that paid roles at the national living wage recognise the contribution. Do not plan provision around free labour.
7. 7Fit the working model to the budget. Mono-, multi- and inter-disciplinary practice are shaped by socio-economic factors, so a single analyst working alone is a different offer from an integrated sport science team. State plainly which one the budget supports, and do not promise interdisciplinary output from a mono-disciplinary budget.
8. 8Run a cheaper option alongside the expensive one. The source's closing chapter pairs the cost of the discipline with 'alternative suggestions for technological investment'. Record what the lower-cost route would deliver, so you can show the coach and the funder what the extra spend actually buys.

The source's own example is the history of analyst labour. Clubs once took on willing volunteers as interns who worked extensive hours for little or no pay, often covering travel costs themselves. For the club this was the cheapest human-resource line, and the source says little thought went to the analyst's welfare or socio-economic status. As national practice and policy shifted, unpaid internships became obsolete, and paid roles at the national living wage appeared. Placement years then became more popular, as undergraduate sandwich years or postgraduate study. Many postgraduate placements are embedded in taught courses where students split time between on-the-job learning and academic study. A club budgeting now has to cost that human line honestly: a paid placement bought through a university partnership, rather than a hidden free-labour subsidy. The same chapter observes that roles in lower league clubs or less well funded sports often promote innovation and real professional growth, so a smaller budget does not by default mean a weaker service. The practical conclusion is to price people at a defensible rate and treat any shortfall in hardware as a design problem rather than a reason for cutting corners on staff.

• If a tool's price is high and you cannot state the specific coaching decision it will change, then do not buy it. The source finds no consistent positive link between technology and coaching outcomes.
• If the plan relies on unpaid or near-unpaid labour to hold delivery together, then treat it as an ethical and sustainability risk and re-cost with paid roles or properly structured placements.
• If your funding is limited relative to other sports or clubs, then prioritise the capability the coach needs live and defer the rest, and use the constraint to drive innovation, as the source observes in lower-funded settings.
• If the team wants an interdisciplinary service, then check that staffing and budget actually allow disciplines to work together, or call the service mono- or multi-disciplinary.
• If a resource is scarce (money, time, support, status), then name which one it is before choosing a solution, because buying kit will not fix a shortage of time or backing.

• Buying on prestige or fear of being left behind. The tell: the purchase rationale cites what other teams use rather than a coaching decision it will improve.
• Costing only hardware and subscriptions. The tell: the budget has no line for analyst time or staffing, and workload is absorbed by one overstretched person.
• Relying on free or underpaid labour as a permanent model. The tell: high turnover of interns or volunteers, and delivery that depends on people working extensive hours for little remuneration.
• Letting technology replace tacit knowledge. The tell: decisions are justified by the data alone, with no input from coach or player experience, which the source calls vital intuition.
• Claiming interdisciplinary depth the resources cannot support. The tell: disciplines still work in silos while the team describes itself as integrated.

• ▸Coaching decisions to support: list the specific decisions analysis must inform, and mark which need live delivery and which can be reviewed afterwards.
• ▸Hardware cost line: list capture and device needs, and the cheapest option that meets each.
• ▸Software subscription cost line: list the platforms required and what each does for the listed decisions.
• ▸Human resource cost line: state who delivers (salaried, placement, postgraduate) and how they are paid.
• ▸Scarce resources: tick which of money, time, status, support and opportunity are limiting, and which is the binding one.
• ▸Working model supported: mono-, multi- or inter-disciplinary, and what in the budget justifies that label.
• ▸Evidence for spend: for each major purchase, state the expected change in coaching behaviour and how you will check it.
• ▸Lower-cost alternative: describe the cheaper route and what it would not deliver.
• ▸Review date and decision: record when you will revisit the spend and who signs off.

1. 1Name the construct to yourself: micropolitics is the day-to-day social reality of staff (people, opinions, attitudes, personalities), and literacy is navigating it deliberately. Treat the workplace as a political system with paradoxes and double binds, not a neutral pipeline from data to decision.
2. 2Audit your own lens first. Write down your subjective educational theory (what you know and believe about the sport and how you do the job) and your personal interpretive framework (the career-built lens through which you read situations). Your read of a tense meeting is filtered through both, so state your assumptions before acting on them.
3. 3Map the stakeholders you must manage relationships with: athletes, coaches, other staff, managers, owners. For each, note what they want from analysis and where your output touches their work. The literature treats managing these micro-relations as a core part of the job, not a distraction from it.
4. 4Study the coach's coaching philosophy, which is not the same as their playing philosophy. Spend quality time with coaches and players, learn the game plan and the technical and tactical preferences, and stay a student of the game. Use this to understand the pedagogical principles and values behind the questions they ask.
5. 5Convert every request into a specific, measurable performance question agreed with the coach. Settle a small set of core questions together. Never deliver a general analysis, because it is too vague and wide-ranging and leaves your value open to any interpretation.
6. 6Manage expectation, time and output across overlapping cycles. You may finish a post-match review while building pre-match work weeks ahead and maintaining long-term profiling. State what is in progress, what is next and what is waiting, so no one assumes you are working only on their item.
7. 7Manage face deliberately. Face is your public self-image, built through interaction, and face-work is the effort to maintain it and earn buy-in. Before a meeting, decide what image your delivery needs to hold (credible, humble, informed), and check that you could still deliver the insight if your presentation were deleted.
8. 8Reduce language friction. Propose a personalised team dictionary of agreed definitions, and lead its creation if the group allows. Shared terms lower the risk that staff or players avoid speaking up for fear of putting their reputation on the line.
9. 9Own your place in the 'so what?' conversation. Know where the coaching cycle you are in (training design, selection, recruitment, talent ID, targets) and whether your message fits it. Carry knowledge from each phase to the next so it reaches decisions.

The passages describe an analyst working in several cycles at once: completing the post-match review for one game, building the pre-match for a game many weeks away, and maintaining long-term profiling. They name micropolitical literacy as what manages expectation, time and output in that position. The following scenario is an illustration built from those elements, not a case from the source. A coach sends three requests in one day, one of them 'a general look at how we played'. The micropolitically literate analyst does not refuse and does not quietly absorb it. They go to the coach and turn it into a specific, measurable performance question, using the core questions already agreed. They tell the coach what is in progress (post-match, pre-match, profiling) and what the new request displaces. They use the shared definitions from the team dictionary so the coach and players read the output the same way. In the meeting, they present with enough sport knowledge that the point survives if the slides fail. The expectation is set, the face held, and the output tied to a decision, which is the 'so what?'.

• If a request is general ('have a look at the game'), then convert it into a specific, measurable question with the coach before any work starts.
• If you hold work in several cycles at once, then state the queue and trade-offs to the coach rather than silently absorbing the load.
• If staff or players use the same term differently or avoid asking what it means, then propose and lead a shared team dictionary.
• If you cannot say where your output feeds the coaching cycle (training design, selection, recruitment, talent ID, targets), then hold it until you can, because a message that does not fit the process will not land.
• If you cannot explain the insight without the slides, then the knowledge is too thin to present, so keep learning the game before you stake your face on it.

• Treating the coaching process as a clean, linear pipeline. The tell: you are surprised by stakeholder pushback and read it as irrational rather than as ordinary micropolitics.
• Delivering general, unfocused analysis. The tell: coaches say 'interesting' and nothing changes in training or selection.
• Working in silo, unaware of the coach's philosophy and values. The tell: your outputs answer questions the coach is not asking, or are framed against their pedagogy.
• Ignoring face and buy-in. The tell: you are right on the data but players and staff discount you, or you avoid contributing for fear of putting your reputation on the line.
• Absorbing every request without signalling capacity. The tell: overlapping post-match, pre-match and profiling work all slip, and expectation about your output goes unmanaged.

• ▸Stakeholder and role: name each person (athlete, coach, other staff, manager, owner) whose decisions your analysis touches.
• ▸Their need from analysis: one line on what they want it to help with.
• ▸Coach's coaching philosophy (not playing philosophy): note the pedagogical principles and values you have observed.
• ▸My interpretive lens: one assumption you hold about this person or situation, and how you could check it.
• ▸Request received: paste the request as it was worded.
• ▸Specific, measurable performance question: rewrite the request as an agreed question.
• ▸Cycle stage and 'so what?': record the stage (post-match, pre-match, profiling, selection, recruitment, talent ID) and the decision the output feeds.
• ▸Workload and expectation: list the work in progress and what this request delays, as told to the coach.
• ▸Shared terms to define: note any terms that need an entry in the team dictionary.
• ▸Face check: note what image the delivery needs to hold, and whether you could deliver it without your slides.

1. 1Learn the coaching context before you deliver anything. The source's premise is that 'to become a better analyst you have to better understand coaching, and vice versa.' Wright et al. (2016) found analysis is widely used but little thought is given to its context or its impact back on players. Write down, for each coach, how they plan, do and review (the 'Plan, Do, Review' model) and where your output enters that loop.
2. 2Set a baseline for each person, not for the squad. Coaches and players are studied separately in the literature you are drawing on (elite coaches, elite football players, semi-elite rugby head coaches), which implies engagement differs by role and level. Ask each coach and athlete three things: what analysis they have had before, what they found useful, and what they ignored.
3. 3Treat past experience as the starting condition. Prior negative experiences shape trust, and the source admits analysts report some negative experiences and that scepticism about the effectiveness of feedback persists. Ask what went wrong last time and design your first deliverable to avoid that specific failure.
4. 4Win credibility through delivery competence first. Butterworth's own early example is turning information around to coaches 'in a highly efficient manner' with unfamiliar equipment. Agree a turnaround time with the coach, hit it on the first three deliveries, and keep the output no larger than the decision it serves.
5. 5Make delivery a conversation, not a drop. Chapter 5 includes a 'Coach/analyst conversation' framework. Show the coach one clip or metric, ask what they would change, and record their answer. Their language then sets the next brief.
6. 6Put athletes in the loop through interactive formats. The source lists live replay in training and an athlete-led unit debrief as working practices. Start with one low-stakes session (a replay at training) and then hand a unit debrief to the athletes to run.
7. 7Adjust for demographics and setting. The source notes analysis now reaches youth, non-elite and women's sport, often through affordable workflows. Do not copy the elite-men's delivery model. Check age, level and resources, and change the format and volume accordingly.
8. 8Review engagement after each block. Check whether the coach asked for the analysis again, whether athletes opened or discussed it, and whether the coach changed anything because of it. Record the evidence, since efficacy is still questioned, and revise the approach.

The source gives one concrete anecdote. At a Hertfordshire Mavericks v Team Bath British Netball Superleague match, Andrew Butterworth was still an undergraduate. He used equipment the observing academic (Peter O'Donoghue) had not seen used in netball, and he turned information around to the coaches 'in a highly efficient manner.' This shows delivery competence earning coach attention before any relationship existed. Applied as practice: an analyst new to a coaching staff agrees one narrow deliverable, such as a clip set within a fixed time after the match, and meets it every time. Only once coaches rely on that does the analyst introduce the conversation format from Chapter 5, asking what the coach would change after seeing the clips. The anecdote does not report coach or athlete engagement data. The sequence (fast, reliable delivery first, then dialogue, then athlete involvement) is an inference from the source's themes, not a documented result.

• If a coach or athlete has had a poor past experience with analysis, then ask what failed and fix that one thing in your first deliverable before introducing anything new.
• If you have not yet shown reliable turnaround, then do not add new metrics or formats. Hit the agreed delivery time first.
• If the coach engages but the athletes do not, then move delivery toward live replay in training or an athlete-led unit debrief rather than sending more files.
• If the setting is youth, non-elite or women's sport with limited resource, then use the simpler, affordable workflow and a smaller output instead of copying an elite template.
• If a coach expresses scepticism about effectiveness, then treat it as a valid position, agree one decision the analysis will inform, and check afterwards whether it did.

• Delivering analysis without understanding the coaching context. Tell: outputs are accurate but never referenced in planning or sessions, which is the context gap Wright et al. (2016) describe.
• Assuming one engagement style fits everyone. Tell: the head coach uses the reports while assistants and athletes never open them.
• Over-delivering early. Tell: large packs arrive late and nobody can say which decision they served, so you lose the credibility that fast turnaround builds.
• Ignoring a person's history with analysis. Tell: polite compliance, no follow-up requests, and passing remarks about 'last time.'
• Never measuring engagement or efficacy. Tell: you cannot say what changed because of your work, so scepticism goes unanswered.

• ▸Person and role: name, coach or athlete, level and age group.
• ▸Past experience with analysis: what they received before, and what they found useful or ignored.
• ▸Prior negative experiences: anything that damaged trust, in their words.
• ▸Where analysis enters their process: the plan, do or review point where your output reaches them.
• ▸Agreed deliverable and turnaround time: one narrow item and the deadline you commit to.
• ▸Delivery format: file, conversation, live replay in training or athlete-led debrief, and why it suits them.
• ▸Contextual adjustments: age, level, resource and sport factors that change the format or volume.
• ▸Engagement evidence after delivery: requests for more, discussion, changes made. Record what you actually observed.
• ▸Next adjustment: one change to make before the next block.

1. 1Start from one specific, measurable performance question agreed with the coach. The source rules out a 'general analysis' as too vague and wide-ranging. Settle on a small number of core questions and write each one down before you cut any footage.
2. 2Agree the delivery terms before the match. For each phase (pre-match, in-game, post-match), fix the turnaround time, the medium, the times of interruption and the technology. Turnaround times must be sensible and realistic given your tools, so check them against your hardware and software first.
3. 3Specify the content of every clip set. The source criticises Horne's (2013) model because 'clips' does not say which clips, what indicators, or how they are presented. Before sending anything, state which clips, which indicators and in what format.
4. 4Shape the message for the end user. The aim is to land it with the end user at the appropriate time. Name who receives it (coach, player or group) and individualise and adapt the method using pedagogical principles of feedback. Aim for the right balance, the right impact and the right group.
5. 5Keep delivery short, sharp and concise. In live situations there may be only a short window, so lead with the one fact that supports or challenges the coach's 'gut' feeling.
6. 6Build in two-way interaction. Horne's continuous 'Interactive Feedback' ring and the largely two-way flow in Mayes et al.'s player-centred cycle are the models. Ask for the coach's or player's response and use it to refine the next cut.
7. 7Know the content well enough to deliver without slides. The source asks whether you understand the game deeply enough to deliver 'even if your presentation got deleted'. Spend time with coaches and players to learn the coach's philosophy and the language they use.
8. 8Check the technical chain, because it affects the quality the end user sees. Higher-definition capture means larger files. Capture conversion boxes (the source names BlackMagic and AJA U-Tap) may not process at the same quality, so the footage in your analysis software may fall below the camera's full 4K quality.

The source cites Franks and Miller (1986), who found coaches recall around 45% of match events, and Laird and Waters (2008), who found 59%. A large share of a match is therefore forgotten unless analysis fills the gap. Take a coach who asks for 'clips' after a game, which is the vagueness the source criticises in Horne's (2013) model. Instead of sending a generic reel, you agree one core question with the coach in advance, for example one tactical area they have flagged. You then specify which clips answer it, which indicator each clip shows, and how they will be presented, and you fix a turnaround time you can meet with your tools. For in-game use you also agree the communication method and the times you may interrupt. At the review you keep the delivery short, ask the coach or player what they see, and adjust the next cut. The source's own illustration of speed is the author turning information around to coaches 'in a highly efficient manner' at a Hertfordshire Mavericks netball match.

• If the coach's request is general (e.g. 'clips' or 'a general analysis'), then convert it into a specific, measurable question and agree it before starting.
• If the feedback is for use live or in-game, then agree in advance the focus, communication method, interruption times and technology, and keep each message to the shortest useful fact.
• If the recipient changes (coach, individual player, group), then individualise the content and method and apply pedagogical principles, rather than reusing one presentation.
• If the agreed turnaround cannot be met with your current tools, then renegotiate the time or the scope before the deadline, since turnaround must be sensible and realistic.
• If footage or a conversion step degrades image quality, then fix the capture chain, because the end user sees the degraded version and the message loses clarity.

• Vague deliverables: sending 'clips' with no stated indicator or purpose. The tell is that the coach asks what they are meant to look at.
• One-way broadcasting: pushing reports out with no response loop. The tell is that nobody comments and the next request looks the same as the last.
• Overlong delivery in live settings: giving detail when only a short window exists. The tell is that the coach cannot act on it before play resumes.
• Late or unrealistic turnaround: promising speeds your tools cannot support. The tell is repeated missed deadlines or rushed output.
• Dependence on the slides: you cannot explain the insight if the presentation disappears. The tell is that you read from the screen rather than from your understanding of the game and the coach's philosophy.

• ▸Core performance question: write the single specific, measurable question this analysis answers.
• ▸End user: name the coach, player or group receiving it, and what they need to do with it.
• ▸Phase and timing: pre-match, in-game or post-match, with the agreed turnaround time.
• ▸Medium and technology: state how it is delivered and the tools required, and confirm they can meet the turnaround.
• ▸Interruption protocol (live only): agree the communication method and the times you may interrupt.
• ▸Clip and indicator specification: list which clips, which indicators, and how they are presented.
• ▸Message in one sentence: the shortest statement of the key insight, supporting or challenging the coach's 'gut' feeling.
• ▸Individualisation note: record how the content or method is adapted to this end user's needs.
• ▸Interaction check: record the end user's response and the change you will make to the next delivery.

1. 1Start from the information deficit, not the software. Write down what the coach cannot recall: the passages cite recall of around 45% of match events (Franks & Miller, 1986) and 59% (Laird & Waters, 2008; Wright et al., 2012 call 59% a favourable estimate). Treat the 41-55% gap as your remit and say so to the coach in those terms. Note the passages flag debate over the exact figures and contextual factors, so present the numbers as indicative.
2. 2Agree the core performance question before you capture anything. Keep that question at the centre of the data and conversation; it stops volume from burying specificity. One question per feedback cycle is the working unit.
3. 3Learn the coach before you build the output. Spend quality time with coaches and players to understand their coaching philosophy (the pedagogical principles and values, not the playing philosophy), the game plan, and their technical and tactical preferences. Test yourself: could you still deliver the insight if your presentation were deleted?
4. 4Secure a capture angle and quality that supports the question. Choose the vantage point that gets the wide angle and the most players in shot (gantry, hi-pod, mast, drone). Know the limits: drone battery lasts about 25 minutes before ascent and descent time is deducted, and 4K quality can degrade at capture conversion boxes (BlackMagic, AJA U-Tap) so check the footage in your software at the quality you expect.
5. 5Log the context with every event. Record opposition, venue, time of day and score line next to the data. The passages state coaches cannot recognise, interpret and weigh these in real time, so the analysis has to carry them for the coach.
6. 6Keep the data objective and reproducible, then separate it from interpretation. Present counts and clips as the unbiased record, and mark your interpretation as a separate layer. This is how the feedback keeps the validity and reliability the passages credit to performance analysis, and avoids the subjectivity Knudson & Morrison (2002) warn about.
7. 7Shorten the loop only where the quality holds. Use near-real-time replay or live coding in training and matches when the question suits it. Speed is worth having only if the clip or figure is still accurate and specific.
8. 8Close the loop with the coach and athletes. Check the output is usable (the coach can act on it) and specific (it answers the question). Use their response to refine the next question, with the analyst acting as critical friend to evolving coaching delivery.

Franks and Miller (1986) found coaches recalled around 45% of match events, and Laird and Waters (2008) found 59% in a repeat study. Take a coach who reviews a match from memory and notes. Roughly half the events have gone, and Hughes and Bartlett (2008) note that the coach's perception of performance becomes distorted by what they can or cannot remember. You agree one performance question with the coach, and code the match against it, logging opposition, venue, time of day and score line alongside each event. You return clips and counts that fill the missing events, and you keep your interpretation visibly separate. Olsen and Larsen (1997) offer a real parallel: computerised analysis for Norway's national side was found to significantly enhance on-pitch performance and helped them use limited resources against the best teams. Manager Egil Olsen said analysis might not be pretty, but it works. The test for your feedback is the same: does it close the specific deficit the coach has?

• If the coach's account of a match conflicts with your data, then check capture and coding quality first, then present the evidence objectively with context rather than argue from opinion.
• If you cannot state the performance question in one sentence, then do not produce output yet; go back to the coach and the game plan.
• If a faster feedback route (live coding, live replay) would cut the accuracy or specificity of the clip, then wait for the validated version.
• If the context (opposition, venue, score line) could change the meaning of a figure, then attach it to the figure before it reaches the coach.
• If the output would not survive without your slides, then you do not understand the play deeply enough; spend more time with the coach, players and the game.

• Reporting volume instead of answering the question. The tell: the coach receives many statistics and cannot say what to change.
• Treating the recall figures as exact. The tell: you quote 45% or 59% as fact when the passages flag debate and contextual influences.
• Stripping context from data. The tell: the coach misreads a result because opposition, venue, time of day or score line is missing.
• Trusting capture quality without checking it. The tell: footage that looked 4K at capture is degraded in your analysis software, or the angle cuts out players.
• Mixing subjective opinion into the objective record. The tell: the coach cannot tell which parts are counted events and which are your reading.
• Ignoring the coach's philosophy. The tell: accurate output that does not fit how the coach teaches, so it goes unused.

• ▸Performance question: write the single question this feedback answers.
• ▸Coach's philosophy and game-plan notes: record the principles, technical and tactical preferences that shape how the answer is framed.
• ▸Coach's recalled events vs coded events: list what the coach remembered against what the data shows, to size the information deficit.
• ▸Context log: opposition, venue, time of day, score line.
• ▸Capture check: angle, vantage point, footage quality confirmed in analysis software (yes/no, notes).
• ▸Objective record: counts, clips and timestamps with no interpretation.
• ▸Analyst interpretation: label clearly as separate from the objective record.
• ▸Usability and specificity test: can the coach act on this tomorrow, and does it answer the question? (yes/no, why)
• ▸Coach and athlete response: what they used, what they ignored, and the next question.

1. 1Agree the purpose with the coach before you record anything. State that the aim is to analyse the coach's own coaching performance, not the athletes', and that you will use the same objective, systematic skills you use for technical and tactical analysis. The coach has to opt in, because behaviour change starts with reflection.
2. 2Learn the instrument before you observe. Work through every behaviour listed in your chosen systematic observation tool (ASUOI, CAIS or the UK Coaching observation checklist) until you can recognise each one live. If you cannot name the behaviour as it happens, you cannot code it reliably.
3. 3Collect the context first. Note the session's timing, environment and level, and the intended outcomes from the session plan or the periodic/seasonal plans. Behaviour only means something when judged against what the session was meant to achieve.
4. 4Capture the session with the tool and with video where it helps. You are collecting behavioural data, the same kind of work as a normal analysis with a different set of data. Record what the coach did, not what you think they meant.
5. 5Reduce the data to the coach's controllable behaviours. Separate what the coach can change (e.g. the behaviours the tool lists) from outcomes they cannot directly control. Then compare the pattern against the session aims: did the behaviours displayed serve the planned outcome?
6. 6Hold the coach/analyst conversation. Present the objective behavioural analysis and let the coach interpret it. Where possible, bring in a critical friend (a coach developer, mentor, another coach or even a player) to deliberate on the data with them.
7. 7Have the coach decide which behaviours to adjust and why. The change must be consistent with their own pedagogy and philosophy, not your preference. Write the chosen behaviour and the intended effect down.
8. 8Re-observe in a later session with the same tool and the same context checks. Compare against the earlier data to see whether the adjusted behaviour has actually changed, and run the cycle again.

This is an illustrative walk-through built from the process described in the source, not a documented case. The source notes that Aston Villa FC and Glasgow Rangers FC employ full-time analysts dedicated to coach behaviour analysis, using the same skills and working processes as a standard analyst. Suppose an analyst joins a coach's training session. Beforehand, they read the session plan and the seasonal goals so they know what the session is meant to achieve, and they confirm they can identify every behaviour in the observation tool. During the session they record the coach's behaviours systematically, with video if useful. Afterwards, they compare the observed behaviours with the session aims and sit down with the coach, perhaps with a coach developer present as a critical friend. The coach reads the data against their own philosophy, picks one behaviour they want to adjust, and the analyst repeats the observation next session to see whether it changed. The source's logic is that more effective coaching delivery ultimately raises athlete performance.

• If the coach has not agreed to be observed, then stop and negotiate purpose first; objective data delivered to an unwilling coach produces defence, not reflection.
• If you cannot identify a behaviour from the tool in real time, then rehearse the tool's behaviour list before observing; do not code on guesswork.
• If the observed behaviours do not match the session plan's intended outcomes, then raise it as a question for the coach to interpret, not as a verdict.
• If the data shows many behaviours to adjust, then have the coach choose the few that fit their pedagogy and philosophy and that they can control.
• If a coach developer, mentor or other coach is available, then include them as a critical friend in reviewing the analysis; do not interpret alone.

• Observing without context: you code behaviours without reading the session plan or seasonal goals. Tell: your feedback cannot say whether the aims were met.
• Analyst-led interpretation: you tell the coach what the data means instead of letting them reflect. Tell: the coach nods but changes nothing, or argues with the coding.
• Tool unfamiliarity: you miss or mislabel behaviours because you have not learned the instrument. Tell: the coach disputes the data and the coding cannot be reproduced.
• One-off observation: you deliver a single report and never re-observe. Tell: there is no evidence either way that any behaviour changed.
• Misaligned change: the adjustments reflect the analyst's idea of good coaching rather than the coach's pedagogy and philosophy. Tell: the coach complies briefly, then reverts.

• ▸Coach and session: name the coach, session and date.
• ▸Context: record timing, environment and level.
• ▸Intended outcomes: copy the aims from the session plan or periodic/seasonal plan.
• ▸Observation tool: state which tool you use (e.g. ASUOI, CAIS, UK Coaching checklist) and confirm you know every behaviour in it.
• ▸Observed behaviours: record the behaviours captured, with video references if used.
• ▸Match to aims: note where behaviours served or did not serve the intended outcomes.
• ▸Critical friend and conversation notes: record who joined the review and the coach's own interpretation.
• ▸Behaviour to adjust: record what the coach chose, with the link to their pedagogy and philosophy.
• ▸Re-observation: record the date of the next session and whether the behaviour changed.

1. 1Name the performance outcome before you touch any data. The construct is improvement in athlete and team performance on the field, so write down which on-field behaviour or result the feedback is meant to change. Feedback with no named target cannot later be linked to improvement.
2. 2Place the analysis inside the coaching process. The source treats coaching as a messy, multidimensional process (the book's models include 'Plan, Do, Review'). Identify which stage your output serves (planning, delivery or review) and who in the coach's process will act on it.
3. 3Secure accuracy and reliability first. The source states coaches need accurate, reliable sources to avoid subjectivity in feedback (Knudson & Morrison, 2002). Check your data and clips before they reach the coach, because analysis-supported feedback is only as credible as its objectivity.
4. 4Match the analysis type to the question. The book separates match-to-match analysis, longer-term trend analysis and profiling (single-match profiles, longitudinal trends, multimedia profiles). Use match analysis for the next-game problem and trends or profiles to judge whether change is holding over time.
5. 5Turn information around at a speed the coach can use. The foreword's example credits Butterworth with turning information around to coaches 'in a highly efficient manner' at a Superleague match. Agree in advance what the coach gets, in what format and by when.
6. 6Choose the delivery route that reaches the athlete. The source's own chapter titles list manual telestration, live replay in training and an athlete-led unit debrief. Pick the one that puts the information in front of players, not only the coach.
7. 7Close the loop back to the players. Wright et al. (2016) found analysis is widely used but little thought is given to the context around it and its impact back to players. After delivery, record what the athletes did with the feedback and what changed in the next performance.
8. 8Treat efficacy as a claim to test, not assume. The foreword admits scepticism remains about whether analysis-based feedback helps athletes improve. Keep a record of feedback given alongside later performance so your contribution is documented, and describe it as 'in part', as the construct does.

The foreword describes Andrew Butterworth, then an undergraduate, working at a British Netball Superleague match between Hertfordshire Mavericks and Team Bath. He used equipment the foreword's author had not previously seen in netball, and he got information to the coaches quickly. That is step 5 done well. The passages give no outcome data, so improvement cannot be claimed from this episode alone. The example shows what the rest of the chain needs. After the match, name the target behaviour the coaches were working on. Note the route the information took (for example live replay in training or a unit debrief). Compare the same indicator in the following matches, using longitudinal trend analysis. Only then can you say the feedback contributed, in part, to the change. Without that comparison, quick turnaround is a service and not yet evidence of improvement.

• If a coach has no named performance target, then agree one before analysing, because nothing can be linked to improvement without it.
• If the data or clips cannot be shown to be accurate and reliable, then hold them back. Subjective or unreliable feedback undermines the whole process.
• If the question concerns the next game, then use match-level analysis. If it concerns whether change is sustained, then use longitudinal trend analysis or profiling.
• If feedback reaches the coach but there is no sign of it reaching or changing the players, then change the delivery route (training replay, telestration or athlete-led debrief) and record the impact on the players.
• If someone asks whether your analysis improved performance, then answer with documented before-and-after indicators and say 'in part', not with a causal claim.

• Counting delivery as improvement: reports go out and tell you nothing about change on the field. The tell is that you cannot name a target behaviour or a later result the feedback was meant to move.
• Ignoring the context around analysis: the source says this is widespread (Wright et al., 2016). The tell is that you do not know how the coach or athletes actually used the feedback.
• Passing on unchecked data: subjectivity creeps back in. The tell is that coaches or players dispute the clips or numbers.
• Feedback that stops at the coach: players never see it in a usable form. The tell is that athletes cannot describe what the analysis said about their own role.
• Overclaiming causation: crediting analysis alone for results in a multi-factor, messy coaching environment. The tell is wording like 'the analysis fixed it', where the source supports only 'in part'.

• ▸Performance target: the on-field behaviour or outcome the feedback aims to change.
• ▸Coaching process stage: Plan, Do or Review, and the coach who will act on the output.
• ▸Analysis type: match analysis, single-match profile, longitudinal trend or multimedia profile.
• ▸Accuracy and reliability check: what you verified before delivery and who confirmed it.
• ▸Delivery route and turnaround: format (telestration, live replay, unit debrief, other), delivery time and recipients.
• ▸Athlete response: what the players did or said after receiving the feedback.
• ▸Next-performance indicator: the same measure from the following match or session.
• ▸Trend note: direction of change across several matches, if applicable.
• ▸Contribution statement: a plain statement of how far the analysis plausibly contributed, 'in part', and what else was acting.

1. 1Write a development-needs list before applying anywhere. The source frames career growth as 'emergent needs, development areas and action planning'. Split it into three columns: technology you cannot yet operate, coaching-process knowledge you lack, and professional skills such as applications and interviews. Turn each gap into one dated action.
2. 2Get applied experience on purpose. The author's own route is 12+ years of work in netball and badminton, including national-team and Team GB roles. Seek repeated, real match and training environments where you run capture, coding and feedback to coaches, and keep a log of what you delivered in each.
3. 3Schedule continuous professional development against technology change. The Foreword's warning is that systems add wireless, live feedback, multi-analyst timelines and new camera systems such as Hi-Pod faster than courses teach them. Every month, list one tool or workflow now in use in the field that you have not tried, and test it.
4. 4Treat each application as a documented product. The source treats application writing and interview preparation as skills to develop. Match each application to the role's coaching context and show evidence of delivered work (an outcome, not a task list).
5. 5Follow up after every outcome, including rejection. Roles are heavily oversubscribed and often give no feedback. Send a short professional follow-up, ask for feedback, and record what you learn in your needs list.
6. 6Build relationships before you need them. The author met Peter O'Donoghue as a rival analyst at a netball game. That contact led to PhD supervision and later to a championed book. Treat opponents, colleagues and academics as long-term contacts, and thank and credit collaborators.
7. 7Run a health-and-safety check on every working environment, not only the venue. The source's own call-to-action case involved real working conditions on coach travel and a temporary stand analysis set-up. Before each trip or fixture, review how you travel, where you set up equipment, and who is responsible for the risk. Raise problems before the event, not after.
8. 8Watch workload and cost. The source flags analysis as an expensive discipline and gives financial issues their own chapter. Match your set-up to what the budget can actually support, and decline or renegotiate demands that push you into unsafe or unsustainable working.

The source gives two real episodes. First, the author's network path: he met Peter O'Donoghue when they were rival analysts at a netball game. Soon after, he started his PhD under O'Donoghue's supervision. O'Donoghue later championed him for this book and co-authored a chapter. The contact was made through applied work, then deepened through study and collaboration, and it carried through to later opportunities. Second, a development signal from the Foreword: a former master's student appeared on YouTube using the Hi-Pod camera system at a training venue, and his former lecturer 'did not teach him how to do that'. Skill growth had continued outside formal teaching, which is the continuous professional development the Foreword calls necessary. Read together, the pattern is: be present in applied settings, keep learning tools the field adopts, and stay visibly connected to people who can speak for you. The passages do not give detail on the health-and-safety incident behind Chapter 9, so do not infer its specifics. What they do show is that unsafe conditions on coach travel and in temporary stand set-ups were serious enough to prompt a book chapter.

• If you are rejected without feedback, then send a courteous follow-up asking for it, because roles are oversubscribed and silence is common. Log whatever you get in your needs list.
• If a tool or workflow is appearing in the field that you cannot use, then schedule hands-on practice now, because technology (wireless, live feedback, new cameras) outpaces formal teaching.
• If an environment (travel, a temporary stand, a set-up) looks unsafe, then flag it and fix it before the session, because the source's call-to-action case came from exactly these settings.
• If you meet someone in an applied setting who works in your area, even a rival analyst, then start a professional relationship, because the author's PhD and book both grew from one such meeting.
• If a role's equipment or workload demand exceeds budget or safe capacity, then raise the cost issue and propose cheaper alternatives, because the source treats analysis as an expensive discipline with options for lower-cost investment.

• Applying in volume without evidence of delivered work. The tell: you collect rejections and no interview invitations, and you cannot name one outcome from past work.
• Letting tool skills go stale. The tell: colleagues or newer graduates use set-ups, such as multi-angle capture or live feedback, that you have never operated.
• Treating rejection as the end of the process. The tell: you never ask for feedback, so the same weaknesses recur in later applications.
• Networking only when you need a job. The tell: your contacts are cold and cannot speak for your applied work.
• Ignoring working conditions because the match is the priority. The tell: you set up in a temporary or awkward position, or travel in poor conditions, and accept it as normal.

• ▸Target role and context: name the role type, sport and level you are aiming for.
• ▸Development needs (technology): list tools or workflows you cannot yet use, with one practice action and date each.
• ▸Development needs (coaching process): list the coaching-process knowledge gaps and how you will close each.
• ▸Applied experience log: record each placement or season, what you delivered and the outcome.
• ▸Application and interview evidence: list the 3 strongest examples of delivered work to use in applications.
• ▸Follow-up record: for each application, note the date sent, the outcome, whether feedback was requested and what you learned.
• ▸Network map: list contacts by relationship (colleague, rival, academic), the last contact date and the next step.
• ▸Health-and-safety check: for each venue or trip, note travel conditions, set-up location and risks flagged to whom.
• ▸Workload and cost review: note current demands, budget limits and one lower-cost or lower-load adjustment.

1. 1Write the pedagogical question before you look at any product. Use the profiling rule: ask 'what are we trying to find out?' and 'why is this piece of analysis so vital?' Get coaches, analysts and, where relevant, players to answer. Example questions from the netball profiling work: who are the strongest starting players, how does performance change after long travel, and what is the effect of game start time or match importance. Do not buy or configure anything until the question is written down.
2. 2Place the question in the coaching process (Plan, Do, Review) and decide when the feedback must arrive. Live during a match, live replay in training, post-session review and athlete-led debrief are different jobs with different technology demands. Wireless systems let information reach coaches live, and let several analysts on different devices enter data to one timeline. Specify whether you need either capability.
3. 3Select the performance indicators first, and check content validity. Choose technical and tactical indicators that can actually answer the coach's question. Refer back to influential academic research where it exists. If the indicators are wrong, no hardware or software can fix the output.
4. 4Match capture hardware to the vantage you need. The passages name Hi-Pod camera systems at training venues, drone footage, and mobile capture through Hudl Focus. Record why each device is used (for example, vantage point or training versus match). Check that the footage supports the indicators you chose.
5. 5Choose coding, telestration and hosting software against the workflow and the end user. The passages list WyScout, Hudl league exchange, manual telestration and hosting systems. Hosting systems can hold match video, statistics and player-coach discussion in one place. Prefer tools that combine indicators into one streamlined medium, since the passages say profiling methods that ignored the end user failed to reach practice.
6. 6Test the system with real data in a real workflow before committing. The passages criticise techniques developed on third-party data that never crossed from the textbook into practice. Run one live session or match and note what breaks in capture, coding, hosting and delivery to the coach or athlete.
7. 7Train the coaches and athletes who will use it. Some coaches hold ill-defined perceptions from past experience, and some see analysis as a threat to their role. Make sure they have relevant training, experience and an understanding of what the technology can offer. Keep explanations plain, because overly complicated language and calculations put potential users off.
8. 8Cost it honestly and plan to review. Pricing can be astronomical, and the passages describe a 'digital divide' affecting lower-funded settings such as women's sport. Prefer a lower-cost option that answers the question over a premium one that does not. Schedule a review, because technology changes quickly and analyst CPD is needed to keep up.

Take the netball profiling context in the passages. The coach's question is: does performance change after long travel, and does game start time matter? Following the profiling rule, you state the what and the why first, then select indicators that can answer that question, such as reliably coded technical and tactical indicators. Only then do you choose technology. You need capture that covers matches in the conditions you want to compare, coding that stores indicator values against match type and context, and a single medium that presents them to the coach. Illustratively, you would pick a hosted video-and-data system so clips and statistics sit together, and you would trial it on a live match before rollout. Charles Reep shows the same logic at the low-tech end. In 1950, with pencil and paper only, he logged 147 attacking plays by Swindon Town in a half. The purpose was clear and the method matched it. Technology is justified by the question it serves, not by how recent it is.

• If you cannot state what you want to find out and why, then do not select or buy technology yet; write the question first.
• If the indicators lack content validity for the coach's question, then fix the indicators before touching hardware or software.
• If feedback is needed live or during training, then specify wireless or live-replay capability and multi-device entry to a shared timeline; if review after the event is enough, then do not pay for live features.
• If the budget is limited, then choose the lowest-cost option that answers the question, such as manual telestration, rather than chasing premium products that deepen the digital divide.
• If coaches or athletes are sceptical or untrained, then train them and simplify the output before adding any new tool.

• Technology-first purchasing: a new system is adopted for its features and no one can say which coaching question it answers. The tell is that outputs pile up with no decisions attached.
• Wrong or unvalidated indicators: the system produces polished data that does not address the coach's concerns. The tell is that coaches ask for different numbers after each report.
• Tools built or tested on third-party data only: the workflow fails under real conditions. The tell is that it works in the demo but not on match day.
• Overly complicated delivery: language, calculations or interfaces put end users off. The tell is that coaches and athletes stop opening the reports.
• Ignoring buy-in and cost: users who see analysis as a threat are not brought along, and expensive kit is bought regardless of funding. The tell is under-used equipment, low coach engagement, and a system nobody maintains or updates.

• ▸Coaching question (what and why): one sentence on what you need to find out and why it matters.
• ▸Coaching-process stage: Plan, Do or Review, and whether feedback is live, in-training or post-event.
• ▸Key performance indicators: list them and note the basis for their content validity.
• ▸Capture hardware: device(s) chosen (for example fixed camera, Hi-Pod, drone, mobile app) and why that vantage serves the indicators.
• ▸Coding and telestration software: tool chosen, and whether manual telestration would be sufficient.
• ▸Hosting and delivery: where video, statistics and discussion live, and how the coach or athlete receives them.
• ▸End-user readiness: who will use it, what training they need, and any scepticism to address.
• ▸Cost and funding fit: total cost, who funds it, and a cheaper alternative that still answers the question.
• ▸Trial result and review date: what broke in the live-data test, and when you will reassess.

1. 1Write the performance question before you open any footage or data. The source is explicit: each piece of analysis needs a specific, measurable question, and a general analysis should never be completed because it is too vague and wide-ranging. Agree a small set of core performance questions with the coach and tie each to one use: selection, opposition analysis, target setting or development.
2. 2Place the profile in the coaching cycle. Plan, Do, Review has no fixed start or end, and the focus changes with each phase, so state whether this profile feeds planning (opposition, targets) or review (the last match, development). A profile built for the wrong phase answers a question nobody is asking.
3. 3Capture context before you capture metrics. Wright et al. (2016) found that analysis is widely used but little consideration is given to context. Record the coach's philosophy (distinct from playing philosophy), the game plan, and the technical and tactical preferences. These tell you what 'good' means in this environment.
4. 4Build two layers. The typical profile is the long-term pattern across matches. The single-match profile is one performance. Tag every match entry with its opposition, conditions and relevant past results (the Franks et al. model explicitly accounts for past results), so a typical profile is never an average of incomparable games.
5. 5Attach multimedia evidence to every claim: a clip or visual beside each number or finding. Then apply the deleted-presentation test from the source: could you still explain the finding, and why it matters to this coach's game plan, if the slides vanished? If not, your understanding is thinner than your output.
6. 6Run parallel cycles deliberately. You may be finishing the post-match for one game, building the pre-match for the next one weeks ahead, and maintaining long-term profiling at the same time. Allocate time to each and set expectations with staff, which the source ties to micropolitical literacy.
7. 7Deliver to the question, then close the loop. Return to the coach with an answer to the agreed question, not a data dump. After the coach reinstructs players, check the next performances. Gutiérrez-Aguilar et al. (2016) report positive performance actions increasing directly after coach reinstruction, so feedback has a visible, trackable effect.
8. 8Use the coach as a critical friend in both directions. Ask what the profile changed in their decisions, and let their answer reshape the next set of questions and the profile structure.

The source gives the scenario, not numbers, so this case is built from it and uses no invented data. An analyst is mid-week with three cycles open: the post-match review of the last game, the pre-match work for an opponent many weeks away, and long-term profiling. She agrees one measurable question with the coach for each, instead of a general analysis. For the pre-match piece, she first records the coach's game plan and technical and tactical preferences, so the opposition profile highlights what matters to this team's approach. She builds the opposition's typical profile and tags each source match with its opposition and conditions, so unusual games are visible rather than averaged away. Each finding gets a clip, so the coach sees the pattern rather than just reading about it. In the meeting she finds the coach's priorities differ from her draft, so she adjusts the questions rather than defending the data. After the next match she checks whether the reinstructed behaviours appear in the single-match profile, and carries the result into the long-term profile.

• If a request is 'analyse the match' or 'profile the opposition' with nothing more, then do not start. Agree a specific, measurable question with the coach first.
• If a match differs from the others in opposition, conditions or past results, then tag it and report it separately from the typical profile instead of averaging it in.
• If you cannot explain a finding without your slides, then you do not understand it deeply enough. Go back to the game plan and the coach's preferences before presenting.
• If several cycles overlap (post-match, pre-match, long-term), then protect time for the long-term profile explicitly, because the urgent match work will otherwise absorb it, and tell the coach what will arrive when.
• If a coach has reinstructed players on a behaviour, then track that behaviour in the next match profile to see whether change followed.

• Context-free profiles: metrics are reported with no opposition, conditions or past-result tags. The tell: coaches ask 'was that just because of who we played?' and you cannot answer.
• General analysis: the output covers everything and answers nothing. The tell: the coach skims, then asks for the one thing they actually needed.
• Misreading the coach's frame: you mistake playing philosophy for coaching philosophy, or ignore the game plan. The tell: correct numbers that the coach dismisses as irrelevant to how the team plays.
• Presentation-dependent understanding: you can only talk through the deck. The tell: you stall when the coach interrupts with a question that is not on a slide.
• Treating the coaching process as a tidy sequence of steps: you assume analysis always lands at a fixed stage. The tell: profiles arrive after the decision has been made, because the cycle is messy and non-linear.

• ▸Core performance question: one specific, measurable question agreed with the coach.
• ▸Intended use: selection, opposition analysis, target setting or development.
• ▸Cycle phase and deadline: where this sits in Plan/Do/Review and when the coach needs it.
• ▸Profile type: typical (long-term), single-match, or both, with the matches included.
• ▸Context tags per match: opposition, conditions, relevant past results.
• ▸Coach and game-plan notes: coaching philosophy, game plan, technical and tactical preferences relevant to the question.
• ▸Evidence pairing: the clip or visual attached to each key finding.
• ▸Deleted-slides summary: the finding and its meaning to this coach in two sentences, written without the deck.
• ▸Follow-up check: the behaviour to re-check in the next match and what the coach decided as a result.

1. 1Place the analysis inside the coaching process first. Use the 'Plan, Do, Review' model (Fig 1.1) as the spine: the session is the 'Do', the coding and conversation are the 'Review', and the output must change the next 'Plan'. If the findings have no route back into planning, the analysis is a one-off audit and not development.
2. 2Let the coach set the question from a learning frame, not from the data. Chapter 5 is built on UK Coaching's coach learning framework and coach learning behaviours (Figs 5.1–5.3). Have the coach name one pedagogical or philosophical area to examine, for example how they give instruction or how they set up practice. The foreword's principle applies: the coaching process should not bend to fit an analysis system; the system supports the process.
3. 3Pick the observation instrument before you record anything. The source names two options: the UK Coaching observation checklist (Fig 5.4) and the Arizona State University observation instrument (Table 5.1). Choose the one whose behaviour categories match the coach's question. Write each category in operational terms so two observers would code the same moment the same way.
4. 4Capture the session so that coach behaviour is visible, not only athlete performance. Use the capture tools the practitioner already has: Hi-Pod or multi-angle capture (Figs 2.1, 2.9) or live replay in training (Fig 2.4). Agree the camera position, session segment and consent with the coach in advance.
5. 5Code the footage against the instrument on a timeline tool (the source lists SportsCode and NacSport timelines, Figs 2.10–2.11). Tag each event to the instrument's categories so every coded instance links back to a clip. Keep clips attached to counts, because the coach will need to see the moments and not only the totals.
6. 6Run the 'coach behaviour analysis cycle' (Fig 5.5) as a repeating loop, not a single report: observe, code, reflect with the coach, agree an action, then observe again against the same instrument. Re-using the same categories is what makes the second observation comparable to the first.
7. 7Hold the coach/analyst conversation (Fig 5.6) with the coach interpreting the footage. Show selected clips, using telestration if useful (Fig 2.16), and ask what the coach intended against what the footage shows. The analyst supplies the objective record; the coach supplies the meaning and decides the pedagogical or philosophical change.
8. 8Set the ground rules for the data before the first session. Chapters 6 and 9 cover micropolitics and ethics. Agree who sees coach-behaviour footage and coding, where it is stored, and that it is developmental and not evaluative of the coach's job.

The source lists the chapter 5 toolkit but does not reproduce case data, so this walkthrough uses an illustrative scenario with no real figures. A coach wants to know whether their sessions match their stated belief in athlete-led learning. Following the 'Plan, Do, Review' model, the analyst and coach place the question in the Review phase and link it to a behaviour area from the UK Coaching learning framework. They select the UK Coaching observation checklist and agree what each category means. The analyst films one training session from an angle that captures the coach and the group, then codes the footage on a timeline against the checklist, tagging each instance to a clip. In the coach/analyst conversation the coach sees the tagged clips and compares what they intended with what the footage shows. They agree one change to try, such as restructuring how a drill is introduced. The cycle then restarts, with the same checklist applied to the next session so the two codings can be compared.

• If the coach cannot state a development question, then do not record yet: use the learning framework to help them choose one behaviour area first.
• If two observers would code the same moment differently, then tighten the category definitions before coding any footage.
• If the coded results do not lead to a specific change in the next session plan, then the review has not reached the 'Plan' stage and you should not close the cycle.
• If a finding is surprising to the coach, then show the clip before the count, and ask for their interpretation before offering yours.
• If the coach is uneasy about who will see the footage, then settle storage and access first; the source treats micropolitics and ethics as part of practice.

• Fitting the coach to the tool: the analyst picks an instrument because it is available, not because it matches the coach's question. The tell: the coach says the report is accurate but irrelevant.
• Counting without clips: the coach is handed totals with no linked footage. The tell: the coach disputes the numbers and nothing is discussed.
• Analyst-led verdicts: the analyst interprets the behaviour for the coach, which turns development into evaluation. The tell: the coach is defensive or silent in the conversation.
• One-off observation: a single session is coded and the cycle is never repeated. The tell: no second dataset exists and no change can be shown.
• Unclear data governance: footage of the coach circulates beyond the agreed people. The tell: the coach stops allowing filming.

• ▸Coach development question: write one pedagogical or philosophical area the coach wants to examine.
• ▸Learning-framework link: name the coach learning behaviour area this question belongs to.
• ▸Instrument chosen: UK Coaching observation checklist or Arizona State University instrument, plus the reason.
• ▸Category definitions: write what counts as each coded behaviour in observable terms.
• ▸Session and capture set-up: date, session segment, camera position and consent confirmed.
• ▸Coding summary: counts per category with the linked clip references.
• ▸Coach's interpretation: record in the coach's words what they intended and what they saw.
• ▸Agreed action for next 'Plan': one specific change to try in the next session.
• ▸Re-observation date and data access: when the same instrument is re-applied, and who may see the footage.

1. 1Start from the premise the source states: the coaching process is a cycle of events in which broad, messy, non-linear decisions are made, driven by social interactions and contextual factors (Franks et al., 1983; Lyle, 1999; Groom et al., 2011; Mackenzie & Cushion, 2013). Write this at the top of your planning notes. Your analysis has to fit inside that process. It does not get to impose a tidier one.
2. 2Classify any process model you are handed as 'for' or 'of' the coaching process. Models 'for' are idealised, built on assumptions and pre-set beliefs. Models 'of' are drawn from observed practice. Treat a 'for' model (Plan, Do, Review; Fairs, 1987; Sherman et al., 1997) as a vocabulary only. Treat an 'of' model (Cote et al., 1995, built from expert gymnastics coaches) as the closer approximation, while remembering that Cushion et al. (2006) still found it too schematic.
3. 3Map your own coach's process by watching it, not by copying a diagram. Record where decisions actually get made, who is in the room, who influences them, and what other personnel and contextual factors are present. Cote et al. highlight coach-athlete interaction, additional personnel and context. Those are your first prompts.
4. 4Do not assume a fixed start point. Miles (2003) notes the process can begin at any point in a cycle, and stages can overlap or merge. Ask the coach where the current cycle effectively started. It may be a planning conversation, a review, or a session already under way. Then slot your deliverable in at that point.
5. 5List the contextual variables for each performance you analyse. The source names opposition, venue, time of day and score line 'to name but a few'. Note which ones you can control for in your analysis and which you can only flag. The source says coaches cannot recognise, interpret and consider all of them in real time, so your output should say which context mattered.
6. 6Identify the micropolitical issues and external influences that may need managing at multiple stages (Butterworth et al., 2013). For each stage where you deliver, note who has a stake in what the data says and who can block, delay or reshape its use.
7. 7Build your systems to support the coach's process, not the reverse. O'Donoghue's foreword says coaching processes should not have to fit a performance analysis system. Systems need to support them. Check regularly that your outputs still match how the coach works now. Technology can streamline coaching, but the process leads.
8. 8Re-map on a schedule. The foreword shows how fast technology and practice move, which makes continuous professional development a necessity. Treat your map of the process as provisional and revisit it whenever the coach's way of working visibly shifts.

Illustration built from the source's own elements, not a documented case. An analyst joins a team and is given the 'Plan, Do, Review' diagram as the working model. The source calls this vague, with no context or description of what happens inside each stage, and it is a model 'for' coaching. The analyst plans to deliver a post-match review pack on a fixed day each cycle. After watching how the coach works, the analyst finds the process does not start at 'Plan'. In line with Miles (2003), it starts wherever the previous match left the coach's thinking, and stages overlap. The analyst then lists the contextual factors for the last performance, namely opposition, venue, time of day and score line, and annotates the pack to show which of them probably shaped the numbers. Because the source says coaches cannot weigh all of these in real time, the annotated pack does part of that work for them. The analyst also notes who else feeds the coach's decisions, following Cote et al.'s emphasis on additional personnel, and adjusts the delivery format to the coach's actual rhythm. The system now supports the process rather than demanding the coach fit it.

• If a model of coaching looks like a neat sequence of steps, then treat it as a model 'for' coaching: useful for vocabulary, unreliable as a description of what this coach does.
• If the coach's decisions seem to start anywhere other than your scheduled input point, then re-time your delivery to the real entry point (Miles, 2003) rather than asking the coach to adapt.
• If a result could be explained by opposition, venue, time of day or score line, then state that context next to the number before it reaches the coach.
• If your system's workflow starts dictating how the coach plans or reviews, then adjust the system. Analysis exists to support coaching processes, not the reverse.
• If a stakeholder's interests are shaping how your analysis is received, then treat it as a micropolitical issue to manage at that stage, not as a data-quality problem.

• Treating Plan-Do-Review as the real process. The tell: your deliverables are timed to idealised stages, and the coach is routinely using information you haven't yet produced or ignoring information you have.
• Assuming a clean start and end to the cycle. The tell: your outputs arrive after the decision was already made, because stages overlapped or merged.
• Presenting numbers stripped of context. The tell: the coach or athletes dismiss a finding by pointing to the opponent, venue or score line you never mentioned.
• Ignoring social and micropolitical drivers. The tell: technically sound analysis goes unused, and decisions follow relationships and influences you never mapped.
• Fitting the coach to the system. The tell: coaches change their routines to suit your software or report format, which reverses the direction of fit the foreword argues for.

• ▸Coach/team and cycle: name the coach, team and the match-to-match cycle you are mapping.
• ▸Observed entry point: where did decision-making in this cycle actually begin, and who started it?
• ▸Overlapping stages: list the stages you saw merge or run at once, e.g. review and planning at the same time.
• ▸Other personnel and influences: who else shapes the coach's decisions (staff, athletes, external parties)?
• ▸Contextual variables: record opposition, venue, time of day, score line and any others that bore on the performance.
• ▸Micropolitical issues: note the stakeholders, interests or pressures that could affect how analysis is received at each stage.
• ▸Current analysis touchpoints: where does your output enter the process today, and does the coach actually use it there?
• ▸Mismatch to fix: where is your system dictating the process rather than supporting it, and what is the adjustment?
• ▸Next re-map date: when you will re-check this map against the coach's actual practice.

1. 1Name your current mode honestly before changing anything. The source says MDT/IDT are widely claimed as aspirations that 'all too often never come to fruition', with practitioners staying in silos as mono-disciplinary practice. For each discipline on the team, ask whether it works alone, runs alongside others, or shares information and decisions with them. Record that as the baseline.
2. 2Write one common performance question in a single sentence and pin it at the centre. The source says that with the volume of data and conversation an interdisciplinary team generates, 'maintaining the core analysis question at the heart' is what keeps the work coherent. Every discipline's output must visibly answer that question or be cut.
3. 3Clarify roles in writing. List what each discipline creates, what it needs from the others, and who delivers to the athlete. Analysts hold the creating, cleaning and visualising of core messages. Evidence from the source suggests only about 13% of analysts deliver directly to players, and about 90% discuss their work with coaches before delivery. So fix the order: analyst and coach align first, then one named person delivers.
4. 4Anchor the analyst-coach relationship first, since the source calls it crucial to the coaching process. Spend time with coaches and players, learn the game plan and the technical and tactical preferences, and understand the plan before producing anything. Run the test from the source: could you still deliver the insight if your presentation were deleted?
5. 5Combine sources before the message leaves the building. The source warns that unintegrated outputs from different technologies or disciplines give a 'disparate and messy outcome'. Before delivery, hold a short joint review where each discipline's finding is placed against the core question. Send one coherent message, not parallel ones.
6. 6Set sharing rules with the tools. Use permissions-based platforms (the source describes Hudl.com permissions for coaches and athletes, Catapult Hub's distribution to the right people, shared live sessions) so each discipline's material reaches only the people who need it. Agree where reports, clips and documents live so nobody works from a private copy.
7. 7Match the delivery to the audience and the moment. The source frames the aim as 'the right balance, for the right impact, to the right group', landing the message at an appropriate time. Before each cycle, decide who receives what, in what form, and when. Players' perceptions of analysis correlate with who delivers and how competently, so choose the deliverer on that basis.
8. 8Keep human judgment in the loop. Treat coach, analyst and player experience (tacit knowledge, intuition) as a team resource that sits alongside the data when final decisions are made. Review each cycle: did the joint output change a decision, and who contributed what?

The source gives no full case study of an IDT, so this example is illustrative and assembled only from mechanisms it describes. A team's common problem is stated as one sentence by the coach and analyst. The analyst has already spent time understanding the game plan and the coach's tactical preferences, as the source advises. Match footage and coded events are uploaded to a permissions-based platform such as Hudl.com or Catapult Hub, so each discipline sees only what it needs. Other disciplines add their findings against the same question. Before anything reaches players, the analyst discusses the combined picture with the coach. The source reports about 90% of analysts do this before delivery. Because players tend to prefer coach delivery, the coach presents the single integrated message, and the analyst stays the gatekeeper who created, cleaned and visualised it. The practical test is the one from the source: the analyst understands the material well enough to deliver it even without the slides. The contrast case is each discipline sending its own report, which produces the 'disparate and messy outcome' the source warns about.

• If each discipline's output can be read with no reference to the shared performance question, then you are working in silos, so rewrite the question and re-cut the outputs until they connect to it.
• If several technologies or disciplines are producing findings on the same issue, then integrate them in a joint review before delivery rather than sending them separately.
• If the athletes' view of analysis depends on who delivers, as the source's evidence suggests, then decide the deliverer deliberately, usually the coach, with the analyst aligned beforehand, unless the analyst has the competence and standing to deliver directly.
• If the analyst does not yet understand the game plan or the coach's preferences, then pause production and build that understanding first, because the coach-analyst relationship underpins the whole process.
• If a tool or dataset is being shared widely by default, then restrict it by role-based permissions so the right people get the right content.

• Calling the setup 'IDT' because job adverts and structures say so, while practitioners still work in silos. The tell: disciplines meet but never change each other's methods or outputs.
• Producing more data than the team can discuss. The tell: long conversations and large reports with no visible link to the core question.
• Parallel messaging, where each discipline or technology tells its own story to the coach or players. The tell: athletes hear mixed or conflicting points about the same issue.
• Analyst-coach misalignment, with the analyst delivering before understanding the plan or talking to the coach. The tell: the coach is surprised by the content, or the analyst can only present with the slides.
• Over-trusting technology and sidelining human experience, or the reverse, resisting tools out of perceived threat to one's role. The tell: decisions cite only dashboards, or colleagues refuse the shared platform to protect current practice.

• ▸Core performance question: write one sentence the whole team agrees to answer.
• ▸Current working mode: mark mono-, multi- or interdisciplinary, with one piece of evidence for the mark.
• ▸Disciplines involved and what each contributes: list the outputs each will create against the question.
• ▸Role clarity: name who creates, who cleans and visualises, and who delivers to athletes.
• ▸Analyst-coach alignment: note the date of the pre-delivery discussion and the game-plan points confirmed.
• ▸Integration point: set the joint review where findings are combined, with date and attendees.
• ▸Sharing and permissions: record the platform used and which groups see which content.
• ▸Delivery plan: state the audience, format and timing of the single integrated message.
• ▸Post-cycle review: record whether a decision changed and what each discipline added.